Particle swarm optimisation aided semi-blind joint maximum likelihood channel estimation and data detection for MIMO systems

Palally, H. R.; Chen, Sheng; Wang, Yao; Hanzo, Lajos

doi:10.1109/ssp.2009.5278578

Cited by 10 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some evolutionary algorithms are the genetic algorithm (GA), repeated weighted boosting search (RWBS), particle swarm optimization (PSO), differential evolution algorithms (DEA), and colony optimization [194,195]. Among those approaches, GA [196][197][198][199][200][201][202], RWBS [203][204][205], and PSO [206][207][208][209] have been applied to channel estimation in multicarrier systems. Evolutionary algorithms are also exploited mainly in pilot pattern placement optimization, which is out of the scope of this work [210,211].…”

Section: Evolutionary Algorithmmentioning

confidence: 99%

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

View full text Add to dashboard Cite

Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

show abstract

Section: Evolutionary Algorithmmentioning

confidence: 99%

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…For wireless systems, evolutionary algorithms (EAs) have been extensively applied both for downlink precoder designs [29][30][31] as well as for uplink MUD designs [32][33][34][35][36][37]. In particular, it has been demonstrated that the EA-aided MUD solutions are capable of approaching the optimal ML-MUD performance at a fraction of the computational complexity imposed by the ML-MUD [32][33][34][35][36][37][38]. Among the various EAs, the differential evolution algorithms (DEAs) [39,40] have been shown to be particularly powerful in joint iterative channel estimation (CE) and MUD.…”

Section: Motivations and Contributionsmentioning

confidence: 99%

Differential Evolution Algorithm Aided Turbo Channel Estimation and Multi-User Detection for G.Fast Systems in the Presence of FEXT

et al. 2018

View full text Add to dashboard Cite

The ever-increasing demand for broadband Internet access has motivated the further development of the digital subscriber line to the G.fast standard in order to expand its operational band from 106 MHz to 212 MHz. Conventional far-end crosstalk (FEXT) based cancellers falter in the upstream transmission of this emerging G.fast system. In this paper, we propose a novel differential evolution algorithm (DEA) aided turbo channel estimation (CE) and multi-user detection (MUD) scheme for the G.fast upstream including the frequency band up to 212 MHz, which is capable of approaching the optimal Cramer-Rao lower bound of the channel estimate, whilst approaching the optimal maximum likelihood (ML) MUD's performance associated with perfect channel state information, and yet only imposing about 5% of its computational complexity. Explicitly, the turbo concept is exploited by iteratively exchanging information between the continuous value-based DEA assisted channel estimator and the discrete valuebased DEA MUD. Our extensive simulations show that 18 dB normalized mean square error gain is attained by the channel estimator and 10 dB signal-to-noise ratio gain can be achieved by the MUD upon exploiting this iteration gain. We also quantify the influence of the CE error, of the copper length and of the impulse noise. Our study demonstrates that the proposed DEA aided turbo CE and MUD

show abstract

“…PSO is a population-based heuristic global optimization algorithm, which originated in modeling the social behavior of bird flocks and fish schools. It has been applied to a variety of technical optimization problems, including channel and parameter estimation [8][9][10][11][12][13] as well as data detection [14] and multiuser detection [15]. Unfortunately, a fair evaluation of PSO is rather difficult due to the wide range of available modifications and the fact that the algorithm is often tuned to optimum performance for a specific optimization problem by empirical measures.…”

Section: Introductionmentioning

confidence: 99%

On Particle Swarm Optimization for MIMO Channel Estimation

Knievel

Hoeher

2012

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Evolutionary algorithms, in particular particle swarm optimization (PSO), have recently received much attention. PSO has successfully been applied to a wide range of technical optimization problems, including channel estimation. However, most publications in the area of digital communications ignore improvements developed by the PSO community. In this paper, an overview of the original PSO is given as well as improvements that are generally applicable. An extension of PSO termed cooperative PSO (CPSO) is applied for MIMO channel estimation, providing faster convergence and, thus, lower overall complexity. Instead of determining the average iterations needed empirically, a method to calculate the maximum number of iterations is developed, which enables the evaluation of the complexity for a wide range of parameters. Knowledge of the required number of iterations is essential for a practical receiver design. A detailed discussion about the complexity of the PSO algorithm and a comparison to a conventional minimum mean squared error (MMSE) estimator are given. Furthermore, Monte Carlo simulations are provided to illustrate the MSE performance compared to an MMSE estimator.

show abstract

Particle swarm optimisation aided semi-blind joint maximum likelihood channel estimation and data detection for MIMO systems

Cited by 10 publications

References 18 publications

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Differential Evolution Algorithm Aided Turbo Channel Estimation and Multi-User Detection for G.Fast Systems in the Presence of FEXT

On Particle Swarm Optimization for MIMO Channel Estimation

Contact Info

Product

Resources

About